Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well
known from computer science is broadly affecting many aspects of various fields including …

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

Mesh-based simulations are central to modeling complex physical systems in many
disciplines across science and engineering. Mesh representations support powerful …

Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Normalizing Flows are generative models which produce tractable distributions where both
sampling and density evaluation can be efficient and exact. The goal of this survey article is …

Normalizing flows are a class of deep generative models that are especially interesting for
modeling probability distributions in physics, where the exact likelihood of flows allows …

We combine amortized neural posterior estimation with importance sampling for fast and
accurate gravitational-wave inference. We first generate a rapid proposal for the Bayesian …

Sampling from known probability distributions is a ubiquitous task in computational science,
underlying calculations in domains from linguistics to biology and physics. Generative …

Phase transitions in a non-perturbative regime can be studied by ab initio Lattice Field
Theory methods. The status and future research directions for LFT investigations of Quantum …